class_docstr: Synchronized BN in FP32
code: def forward(self, x: Tensor, *args, **kwargs) -> Tensor:
      in_dtype = x.dtype
      return super().forward(x.to(dtype=torch.float)).to(dtype=in_dtype)
  
file_path: ml-cvnets/cvnets/layers/normalization/sync_batch_norm.py
imports: from typing import Optional
  import torch
  from torch import Tensor, nn
  from cvnets.layers.normalization import register_norm_fn
label_desc: Use image node_id 2 for calling the SyncBatchNormFP32 obj's underlying member method code with example usage: obj.forward(x) and returns: super